CN111061714A - Timestamp repairing method and device - Google Patents
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Abstract
The embodiment of the invention provides a timestamp repairing method and a timestamp repairing device, wherein the method comprises the following steps: performing anomaly detection on the data points based on a density anomaly detection algorithm to obtain anomaly data point set information; analyzing the abnormal data point set information based on any optimal solving method to obtain target timestamp modification information; and performing timestamp repairing on the abnormal data point set information according to the target timestamp modification information to obtain repaired timestamp attribute information. The data points are marked with abnormal data points through a density abnormality detection algorithm, an abnormal data point set is obtained, the minimum timestamp is repaired aiming at the abnormal data points, so that the abnormal data points are repaired, the repaired abnormal data points are deleted from the abnormal data point set information and added into the normal data points, the distribution and the density of the data points are changed, and the problem of information loss caused by overlarge difference between the repaired data and the original data is solved.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a timestamp repairing method and apparatus.
Background
In recent years, with the development of information technology, the number of categories has been increasing. How to reasonably utilize such massive data has become a key problem in academic and industrial research, and since various data will deviate due to various reasons in the life cycle, the finally obtained data will have abnormal conditions such as inconsistency, incompleteness, inaccuracy and the like, and the existence of these abnormal data will inevitably affect the related algorithms and the analysis results finally obtained by the data analysis software tool. In practice, the loss due to data quality problems is not a trivial concern.
Data quality has become an important research direction, and it is an essential process before data analysis to clean data to obtain high-quality data, and in the prior art, abnormal data restoration of time series data mainly includes abnormal detection and then smooth processing of abnormal data, but this may cause too large difference between restored data and initial data, and information loss.
Therefore, how to more effectively repair the timing data has become an urgent problem to be solved in the industry.
Disclosure of Invention
Embodiments of the present invention provide a timestamp repairing method and apparatus, so as to solve the technical problems mentioned in the foregoing background art, or at least partially solve the technical problems mentioned in the foregoing background art.
In a first aspect, an embodiment of the present invention provides a timestamp repairing method, including:
performing anomaly detection on the data points based on a density anomaly detection algorithm to obtain anomaly data point set information;
analyzing the abnormal data point set information based on any optimal solving method to obtain target timestamp modification information;
and performing timestamp repairing on the abnormal data point set information according to the target timestamp modification information to obtain repaired timestamp attribute information.
More specifically, the density anomaly detection algorithm includes: a density-based clustering algorithm with noise or a local anomaly factor algorithm.
More specifically, the step of performing anomaly detection on the data points by using a density anomaly detection algorithm to obtain anomaly data point set information specifically includes:
calculating the abnormality degree of the data points by using an abnormality degree calculation index method of the local abnormality factor algorithm to obtain an abnormality degree index of each data point;
and marking the data points with the abnormality index larger than the preset threshold as abnormal data points, and storing the abnormal data points into an abnormal data point set to obtain abnormal data point set information.
More specifically, the step of analyzing the abnormal data point set information based on any optimal solution method to obtain the target timestamp modification information specifically includes:
acquiring timestamp attribute information of each abnormal data point in the abnormal data point set information;
and acquiring modified time stamp attribute information of the abnormal data point, and analyzing the difference between the time stamp attribute information of the abnormal data point and the modified time stamp attribute information of the abnormal data point by any optimal solving method to obtain the modified information of the target time stamp.
More specifically, the step of repairing the abnormal data according to the repaired timestamp attribute information specifically includes:
acquiring repaired abnormal data point information;
and removing the repaired abnormal data point information from the abnormal data points, and adding the repaired abnormal data point information into the normal data point set to obtain a data repairing result.
More specifically, the step of calculating the abnormality degree of the data points by the abnormality degree calculation index method of the local abnormality factor algorithm includes:
where ρ (p)i) Representing a data point piLocal achievable density of, Nk(pi) To data point piIs less than or equal to the data point piP' is Nk(pi) The data points in (1).
More specifically, the step of analyzing the difference between the timestamp attribute information of the abnormal data point and the modified timestamp attribute information of the abnormal data point by using any optimal solution method specifically includes:
argmin(Δti)＝{ti ′|Δti＝|ti ′-ti|,LOFk(p′i)≤1}
wherein, Δ ti＝|t′i-tiL is timestamp modification information, where t'iIs a modified anomaly data store p'iTime stamp attribute of, tiIs an abnormal data point piThe timestamp attribute value of (2).
In a second aspect, an embodiment of the present invention provides a timestamp repairing apparatus, including:
the anomaly detection module is used for carrying out anomaly detection on the data points based on a density anomaly detection algorithm to obtain anomaly data point set information;
the analysis module is used for analyzing the abnormal data point set information based on any optimal solving method to obtain target timestamp modification information;
and the repairing module is used for performing timestamp repairing on the abnormal data point set information according to the target timestamp modification information to obtain repaired timestamp attribute information.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the timestamp repairing method according to the first aspect when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the timestamp repairing method according to the first aspect.
According to the time stamp repairing method and device provided by the embodiment of the invention, the abnormal data points are marked through the density abnormality detection algorithm, the abnormal data point set is obtained, the minimum time stamp repairing is carried out on the abnormal data points, the abnormal data points are repaired, the repaired abnormal data points are deleted from the abnormal data point set information and are added into the normal data points, the distribution and density of the data points are changed, and the problem of information loss caused by overlarge difference between the repaired data and the original data is solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a timestamp recovery method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a data repair result according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a timestamp recovery apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a timestamp recovery method described in an embodiment of the present invention, as shown in fig. 1, including:
step S1, carrying out anomaly detection on the data points based on a density anomaly detection algorithm to obtain anomaly data point set information;
step S2, analyzing the abnormal data point set information based on any optimal solving method to obtain target timestamp modification information;
and step S3, performing timestamp repairing on the abnormal data point set information according to the target timestamp modification information to obtain repaired timestamp attribute information, and performing abnormal data repairing according to the repaired timestamp attribute information.
Specifically, the Density anomaly detection algorithm described in the embodiment of the present invention may refer to a Noise-Based Density Clustering algorithm (DBSCAN) or a Local Outlier Factor algorithm (LOF).
The abnormal data point set information described in the embodiment of the present invention refers to a set of data points of the determined abnormal data.
Specifically, the abnormal degree index of each data point is determined through an abnormal degree index calculation method in a density abnormal detection algorithm, the abnormal degree index of each data point is compared with a preset abnormal degree threshold, the data points of which the abnormal degree indexes are larger than the preset abnormal degree threshold are marked as abnormal data points, all the abnormal data points marked as the abnormal points are stored in an abnormal data point set in a centralized mode, and abnormal data point set information is obtained.
And on the basis of obtaining the abnormal data point set information, obtaining the timestamp attribute information of each abnormal data point in the abnormal data point set information, and analyzing the difference between the timestamp attribute information of the abnormal data point and the modified time stamp attribute information of the abnormal data point by using any optimal solving method to obtain target timestamp modification information.
The target timestamp modification information described herein is to modify timestamp attributes of all abnormal data points of the abnormal data point set information through the target timestamp modification information, so that the abnormality degree of all abnormal data points can meet the requirement of the preset abnormality degree threshold.
And after the target timestamp modification information is obtained, the timestamp attribute information of each abnormal data point in the abnormal data point set is repaired through the target timestamp modification information to obtain repaired timestamp attribute information, and the repaired data point is stored as a normal data point to serve as a final data repairing result.
According to the embodiment of the invention, the abnormal data points are marked through the density abnormality detection algorithm, the abnormal data point set is obtained, the minimum timestamp repair is carried out aiming at the abnormal data points, so that the abnormal data points are repaired, the repaired abnormal data points are deleted from the abnormal data point set information and are added into the normal data points, the distribution and density of the data points are changed, and the problem of information loss caused by overlarge difference between the repaired data and the original data is avoided.
On the basis of the above embodiment, the density anomaly detection algorithm includes: a density-based clustering algorithm with noise or a local anomaly factor algorithm.
Specifically, the density anomaly detection algorithm described in the embodiment of the present invention is not limited to the above two algorithms, and the embodiment of the present invention does not limit this.
The density-based clustering algorithm with noise described herein specifically comprises the steps of:
given a data point piIs in the field N∈(pi) Defined as the sum of all data points and data point piSet of data points whose distance is less than or equal to ∈ if | N∈(pi) If | is greater than or equal to a given threshold value MinPts, the data point piIs a core point; a given direct density is achievable by defining as if the data point pj∈N∈(pi) And data point piIs the core point, then the data point pj、piDirect density is achievable if the sequence p is presentj、pv、…、pu、piIf two adjacent data points are directly accessible, the data point pj、piThe density can be reached. A given density connection is defined as if there were a data point ptHas pj、piAll can reach its density, then pj、piThe densities are connected. And (4) solving the maximum data point cluster existing in the whole data point set through the definition of density connection, and marking the data points not in the maximum data point cluster as abnormal data points.
The step of performing anomaly detection on the data points through a density anomaly detection algorithm to obtain anomaly data point set information specifically comprises the following steps:
calculating the abnormality degree of the data points by using an abnormality degree calculation index method of the local abnormality factor algorithm to obtain an abnormality degree index of each data point;
and marking the data points with the abnormality index larger than the preset threshold as abnormal data points, and storing the abnormal data points into an abnormal data point set to obtain abnormal data point set information.
Specifically, the preset threshold described in the embodiment of the present invention refers to a preset abnormality degree preset threshold, which may be specifically set according to actual situations.
The method for calculating the index of the degree of abnormality of the local abnormality factor algorithm described in the embodiment of the invention specifically includes:
where ρ (p)i) Representing a data point piLocal achievable density of, Nk(pi) To data point piIs less than or equal to the data point piP' is Nk(pi) The data points in (1).
The abnormal degree calculation index method of the local abnormal factor algorithm is used for respectively calculating the abnormal degree of each data point to obtain the abnormal degree index of each data point, the data points with the abnormal degree index larger than a preset threshold are marked as abnormal data points, and the abnormal data points are stored in an abnormal data point set to obtain abnormal data point set information.
According to the embodiment of the invention, the data points are subjected to the abnormality degree calculation by the abnormality degree calculation index method of the local abnormality factor algorithm, so that abnormal data points are effectively screened out, and the subsequent steps are facilitated.
On the basis of the above embodiment, the step of analyzing the abnormal data point set information based on any optimal solution method to obtain the target timestamp modification information specifically includes:
acquiring timestamp attribute information of each abnormal data point in the abnormal data point set information;
and acquiring modified time stamp attribute information of the abnormal data point, and analyzing the difference between the time stamp attribute information of the abnormal data point and the modified time stamp attribute information of the abnormal data point by any optimal solving method to obtain the modified information of the target time stamp.
Specifically, the modified abnormal data point timestamp attribute information described in the embodiment of the present invention refers to a pre-established parameter, which is not a constant value, t'iIs a modified anomaly data store p'iTime stamp attribute information t 'assuming modified abnormal data point'iThen, the difference value between the time stamp attribute information of the abnormal data point and the modified time stamp attribute information of the abnormal data point, namely the absolute value of the time stamp modification information is delta ti＝|t′i-ti|，tiIs an abnormal data point piThe minimum value of the timestamp modification information is solved through any optimal solving method:
argmin(Δti)＝{ti ′|Δti＝|ti ′-ti|,LOFk(p′ i)≤1}
wherein, Δ ti＝|t′i-tiL is timestamp modification information, where t'iIs a modified anomaly data store p'iTime stamp attribute of, tiIs an abnormal data point piThe timestamp attribute value of (2).
The minimum value of the time stamp modification information at this time is the target time stamp modification information.
On the basis of the above embodiment, the step of performing abnormal data repair according to the repaired timestamp attribute information specifically includes:
acquiring repaired abnormal data point information;
and removing the repaired abnormal data point information from the abnormal data points, and adding the repaired abnormal data point information into the normal data point set to obtain a data repairing result.
According to the embodiment of the invention, the abnormal data points are marked through the density abnormality detection algorithm, the abnormal data point set is obtained, the minimum timestamp repair is carried out aiming at the abnormal data points, so that the abnormal data points are repaired, the repaired abnormal data points are deleted from the abnormal data point set information and are added into the normal data points, the distribution and density of the data points are changed, and the problem of information loss caused by overlarge difference between the repaired data and the original data is avoided.
Fig. 2 is a schematic diagram of a data repair result according to an embodiment of the present invention, as shown in fig. 2, when repairing of all abnormal data points is completed, that is, when an abnormal point set is empty, the repaired data points and normal data points are saved as a final data repair result,
fig. 3 is a schematic structural diagram of a timestamp recovery apparatus according to an embodiment of the present invention, as shown in fig. 3, including: an anomaly detection module 310, an analysis module 320, a repair module 330, and an information acquisition module 340; the anomaly detection module 310 is configured to perform anomaly detection on the data points based on a density anomaly detection algorithm to obtain anomaly data point set information; the analysis module 320 is configured to analyze the abnormal data point set information based on any optimal solution method to obtain target timestamp modification information; the repairing module 330 is configured to perform timestamp repairing on the abnormal data point set information according to the target timestamp modification information, so as to obtain repaired timestamp attribute information. The apparatus provided in the embodiment of the present invention is used for executing the above method embodiments, and for details of the process and the details, reference is made to the above embodiments, which are not described herein again.
According to the embodiment of the invention, the abnormal data points are marked through the density abnormality detection algorithm, the abnormal data point set is obtained, the minimum timestamp repair is carried out aiming at the abnormal data points, so that the abnormal data points are repaired, the repaired abnormal data points are deleted from the abnormal data point set information and are added into the normal data points, the distribution and density of the data points are changed, and the problem of information loss caused by overlarge difference between the repaired data and the original data is avoided.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 4, the electronic device may include: a processor (processor)410, a communication interface (communication interface)420, a memory (memory)430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 are communicated with each other via the communication bus 440. The processor 410 may call logic instructions in the memory 430 to perform the following method: performing anomaly detection on the data points based on a density anomaly detection algorithm to obtain anomaly data point set information; analyzing the abnormal data point set information based on any optimal solving method to obtain target timestamp modification information; and performing timestamp repairing on the abnormal data point set information according to the target timestamp modification information to obtain repaired timestamp attribute information, and performing abnormal data repairing according to the repaired timestamp attribute information.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
An embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer can execute the methods provided by the above method embodiments, for example, the method includes: performing anomaly detection on the data points based on a density anomaly detection algorithm to obtain anomaly data point set information; analyzing the abnormal data point set information based on any optimal solving method to obtain target timestamp modification information; and performing timestamp repairing on the abnormal data point set information according to the target timestamp modification information to obtain repaired timestamp attribute information, and performing abnormal data repairing according to the repaired timestamp attribute information.
Embodiments of the present invention provide a non-transitory computer-readable storage medium storing server instructions, where the server instructions cause a computer to execute the method provided in the foregoing embodiments, for example, the method includes: performing anomaly detection on the data points based on a density anomaly detection algorithm to obtain anomaly data point set information; analyzing the abnormal data point set information based on any optimal solving method to obtain target timestamp modification information; and performing timestamp repairing on the abnormal data point set information according to the target timestamp modification information to obtain repaired timestamp attribute information, and performing abnormal data repairing according to the repaired timestamp attribute information.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of timestamp recovery, comprising:
performing anomaly detection on the data points based on a density anomaly detection algorithm to obtain anomaly data point set information;
analyzing the abnormal data point set information based on any optimal solving method to obtain target timestamp modification information;
and performing timestamp repairing on the abnormal data point set information according to the target timestamp modification information to obtain repaired timestamp attribute information, and performing abnormal data repairing according to the repaired timestamp attribute information.
2. The timestamp recovery method of claim 1, wherein the density anomaly detection algorithm comprises: a density-based clustering algorithm with noise or a local anomaly factor algorithm.
3. The timestamp retrieval method according to claim 2, wherein the step of performing anomaly detection on the data points by using a density anomaly detection algorithm to obtain anomaly data point set information specifically includes:
calculating the abnormality degree of the data points by using an abnormality degree calculation index method of the local abnormality factor algorithm to obtain an abnormality degree index of each data point;
and marking the data points with the abnormality index larger than the preset threshold as abnormal data points, and storing the abnormal data points into an abnormal data point set to obtain abnormal data point set information.
4. The timestamp retrieval method according to claim 1, wherein the step of analyzing the abnormal data point set information based on any optimal solution method to obtain the target timestamp modification information specifically includes:
acquiring timestamp attribute information of each abnormal data point in the abnormal data point set information;
and acquiring modified time stamp attribute information of the abnormal data point, and analyzing the difference between the time stamp attribute information of the abnormal data point and the modified time stamp attribute information of the abnormal data point by any optimal solving method to obtain the modified information of the target time stamp.
5. The timestamp repairing method according to claim 1, wherein the step of repairing the abnormal data according to the repaired timestamp attribute information specifically includes:
acquiring repaired abnormal data point information;
and removing the repaired abnormal data point information from the abnormal data points, and adding the repaired abnormal data point information into the normal data point set to obtain a data repairing result.
6. The timestamp retrieval method according to claim 3, wherein the step of calculating the degree of abnormality of the data points by the degree of abnormality calculation index method of the local abnormality factor algorithm includes:
where ρ (p)i) Representing a data point piLocal achievable density of, Nk(pi) To data point piIs less than or equal to the data point piP' is Nk(pi) The data points in (1).
7. The timestamp retrieval method according to claim 4, wherein the step of analyzing the difference between the timestamp attribute information of the abnormal data point and the modified timestamp attribute information of the abnormal data point by any optimal solution method specifically comprises:
argmin(Δti)＝{t′i|Δti＝|t′i-ti|，LOFk(p′i)≤1}
wherein, Δ ti＝|t′i-tiL is timestamp modification information, where t'iIs a modified anomaly data store p'iTime stamp attribute of, tiIs an abnormal data point piThe timestamp attribute value of (2).
8. A timestamp recovery apparatus, comprising:
the anomaly detection module is used for carrying out anomaly detection on the data points based on a density anomaly detection algorithm to obtain anomaly data point set information;
the analysis module is used for analyzing the abnormal data point set information based on any optimal solving method to obtain target timestamp modification information;
and the repairing module is used for performing timestamp repairing on the abnormal data point set information according to the target timestamp modification information to obtain repaired timestamp attribute information.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the timestamp recovery method according to any of claims 1 to 7 when executing the program.
10. A non-transitory computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the timestamp recovery method according to any one of claims 1 to 7.
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Application publication date: 20200424 |